Prediction of Customer Attrition Using Feature Extraction Techniques and Its Performance Assessment Through Dissimilar Classifiers

Dimensionality reduction is the process of identifying insignificant data variables and dropping them. The process culminates in obtaining a set of principal variables. Dimensionality reduction not only removes the redundant features, also reduces storage and computation time. Feature Selection and Feature extraction are the two components in dimensionality reduction. This paper explores techniques used for feature extraction and analyzes the results by applying the techniques to customer churn dataset. The performance of these techniques in different classifiers is also compared and results are visualized in graphs.

[1]  Variable Selection in Nonlinear Principal Component Analysis , 2016 .

[2]  Soteris A. Kalogirou,et al.  Machine learning methods for solar radiation forecasting: A review , 2017 .

[3]  Hany Yan,et al.  Unsupervised Dimensionality Reduction for High-Dimensional Data Classification , 2017 .

[4]  Tadashi Araki,et al.  PCA-based polling strategy in machine learning framework for coronary artery disease risk assessment in intravascular ultrasound: A link between carotid and coronary grayscale plaque morphology , 2016, Comput. Methods Programs Biomed..

[5]  Masahiro Kuroda,et al.  Nonlinear Principal Component Analysis and Its Applications , 2016 .

[6]  Michael G. Madden,et al.  The Effect of Principal Component Analysis on Machine Learning Accuracy with High Dimensional Spectral Data , 2005, SGAI Conf..

[7]  Roshan Joy Martis,et al.  Machine intelligent diagnosis of ECG for arrhythmia classification using DWT, ICA and SVM techniques , 2015, 2015 Annual IEEE India Conference (INDICON).

[8]  Prateek Mittal,et al.  Dimensionality Reduction as a Defense against Evasion Attacks on Machine Learning Classifiers , 2017, ArXiv.

[9]  Daniel A. Keim,et al.  Visual Interaction with Dimensionality Reduction: A Structured Literature Analysis , 2017, IEEE Transactions on Visualization and Computer Graphics.

[10]  Francesc Pozo Montero,et al.  Damage and fault detection of structures using principal component analysis and hypothesis testing , 2018 .

[11]  M. Smith,et al.  Machine Learning Classification of SDSS Transient Survey Images , 2014, ArXiv.

[12]  Thea Radüntz,et al.  Automated EEG artifact elimination by applying machine learning algorithms to ICA-based features , 2017, Journal of neural engineering.

[13]  Rong Zheng,et al.  Detecting Stealthy False Data Injection Using Machine Learning in Smart Grid , 2017, IEEE Syst. J..

[14]  M. Navyasri,et al.  Robust Features for Emotion Recognition from Speech by Using Gaussian Mixture Model Classification , 2017 .